Symmetric vs asymmetric protection levels in SDC methods for tabular data

The final publication is available at link.springer.comProtection levels on sensitive cells—which are key parameters of any statistical disclosure control method for tabular data—are related to the difficulty of any attacker to recompute a good estimation of the true cell values. Those protection levels are two numbers (one for the lower protection, the other for the upper protection) imposing a safety interval around the cell value, that is, no attacker should be able to recompute an estimate within such safety interval. In the symmetric case the lower and upper protection levels are equal; otherwise they are referred as asymmetric protection levels. In this work we empirically study the effect of symmetry in protection levels for three protection methods: cell suppression problem (CSP), controlled tabular adjustment (CTA), and interval protection (IP). Since CSP and CTA are mixed integer linear optimization problems, it is seen that the symmetry (or not) of protection levels affect to the CPU time needed to compute a solution. For IP, a linear optimization problem, it is observed that the symmetry heavily affects to the quality of the solution provided rather than to the solution time.Peer ReviewedPostprint (author's final draft

Transfer-Expanded Graphs for On-Demand Multimodal Transit Systems

This paper considers a generalization of the network design problem for On-Demand Multimodal Transit Systems (ODMTS). An ODMTS consists of a selection of hubs served by high frequency buses, and passengers are connected to the hubs by on-demand shuttles which serve the first and last miles. This paper generalizes prior work by including three additional elements that are critical in practice. First, different frequencies are allowed throughout the network. Second, additional modes of transit (e.g., rail) are included. Third, a limit on the number of transfers per passenger is introduced. Adding a constraint to limit the number of transfers has a significant negative impact on existing Benders decomposition approaches as it introduces non-convexity in the subproblem. Instead, this paper enforces the limit through transfer-expanded graphs, i.e., layered graphs in which each layer corresponds to a certain number of transfers. A real-world case study is presented for which the generalized ODMTS design problem is solved for the city of Atlanta. The results demonstrate that exploiting the problem structure through transfer-expanded graphs results in significant computational improvements.Comment: 9 pages, 4 figure

Constraint Generation Algorithm for the Minimum Connectivity Inference Problem

Given a hypergraph $H$, the Minimum Connectivity Inference problem asks for a graph on the same vertex set as $H$ with the minimum number of edges such that the subgraph induced by every hyperedge of $H$ is connected. This problem has received a lot of attention these recent years, both from a theoretical and practical perspective, leading to several implemented approximation, greedy and heuristic algorithms. Concerning exact algorithms, only Mixed Integer Linear Programming (MILP) formulations have been experimented, all representing connectivity constraints by the means of graph flows. In this work, we investigate the efficiency of a constraint generation algorithm, where we iteratively add cut constraints to a simple ILP until a feasible (and optimal) solution is found. It turns out that our method is faster than the previous best flow-based MILP algorithm on random generated instances, which suggests that a constraint generation approach might be also useful for other optimization problems dealing with connectivity constraints. At last, we present the results of an enumeration algorithm for the problem.Comment: 16 pages, 4 tables, 1 figur

Decomposition techniques with mixed integer programming and heuristics for home healthcare planning

We tackle home healthcare planning scenarios in the UK using decomposition methods that incorporate mixed integer programming solvers and heuristics. Home healthcare planning is a difficult problem that integrates aspects from scheduling and routing. Solving real-world size instances of these problems still presents a significant challenge to modern exact optimization solvers. Nevertheless, we propose decomposition techniques to harness the power of such solvers while still offering a practical approach to produce high-quality solutions to real-world problem instances. We first decompose the problem into several smaller sub-problems. Next, mixed integer programming and/or heuristics are used to tackle the sub-problems. Finally, the sub-problem solutions are combined into a single valid solution for the whole problem. The different decomposition methods differ in the way in which subproblems are generated and the way in which conflicting assignments are tackled (i.e. avoided or repaired). We present the results obtained by the proposed decomposition methods and compare them to solutions obtained with other methods. In addition, we conduct a study that reveals how the different steps in the proposed method contribute to those results. The main contribution of this paper is a better understanding of effective ways to combine mixed integer programming within effective decomposition methods to solve real-world instances of home healthcare planning problems in practical computation time

Значение мотивации персонала на предприятии

Основная цель – систематизировать сведения о мотивации персонала и его значении на предприятии

Large-scale unit commitment under uncertainty: an updated literature survey

The Unit Commitment problem in energy management aims at finding the optimal production schedule of a set of generation units, while meeting various system-wide constraints. It has always been a large-scale, non-convex, difficult problem, especially in view of the fact that, due to operational requirements, it has to be solved in an unreasonably small time for its size. Recently, growing renewable energy shares have strongly increased the level of uncertainty in the system, making the (ideal) Unit Commitment model a large-scale, non-convex and uncertain (stochastic, robust, chance-constrained) program. We provide a survey of the literature on methods for the Uncertain Unit Commitment problem, in all its variants. We start with a review of the main contributions on solution methods for the deterministic versions of the problem, focussing on those based on mathematical programming techniques that are more relevant for the uncertain versions of the problem. We then present and categorize the approaches to the latter, while providing entry points to the relevant literature on optimization under uncertainty. This is an updated version of the paper "Large-scale Unit Commitment under uncertainty: a literature survey" that appeared in 4OR 13(2), 115--171 (2015); this version has over 170 more citations, most of which appeared in the last three years, proving how fast the literature on uncertain Unit Commitment evolves, and therefore the interest in this subject